Journal of Mammalogy, 94(4):000–000, 2013
Estimating Amur tiger (Panthera tigris altaica) kill rates and potential
consumption rates using global positioning system collars
CLAYTON S. MILLER,* MARK HEBBLEWHITE, YURI K. PETRUNENKO, IVAN V. SERYODKIN, NICHOLAS J. DECESARE,
JOHN M. GOODRICH, AND DALE. G. MIQUELLE
Wildlife Biology Program, Department of Ecosystem and Conservation Sciences, College of Forestry and Conservation,
University of Montana, Missoula, MT 59812, USA (CSM, MH, NJD)
Wildlife Conservation Society, Bronx, NY 10460, USA (CSM, JMG, DGM)
Pacific Geographical Institute, Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia 690041
(YKP, IVS)
* Correspondent: [email protected]
The International Union for Conservation of Nature has classified all subspecies of tigers (Panthera tigris) as
endangered and prey depletion is recognized as a primary driver of declines. Prey depletion may be particularly
important for Amur tigers (P. t. altaica) in the Russian Far East, living at the northern limits of their range and
with the lowest prey densities of any tiger population. Unfortunately, rigorous investigations of annual prey
requirements for any tiger population are lacking. We deployed global positioning system (GPS) collars on
Amur tigers during 2009–2012 to study annual kill rates in the Russian Far East. We investigated 380 GPS
location clusters and detected 111 kill sites. We then used logistic regression to model both the probability of a
kill site at location clusters and the size of prey species at kill sites according to several spatial and temporal
cluster covariates. Our top model for predicting kill sites included the duration of the cluster in hours and cluster
fidelity components as covariates (overall classification success 86.3%; receiver operating characteristic score of
0.894). Application of the model to all tiger GPS data revealed that Amur tigers in this study made a kill once
every 6.5 days (95% confidence interval [95% CI] 5.9–7.2 days) and consumed an estimated average of 8.9 kg of
prey biomass per day (95% CI 8.8–9.0 kg/day). The success of efforts to reverse tiger declines will be at least
partially determined by wildlife managers’ ability to conserve large ungulates at adequate densities for
recovering tiger populations.
Key words: Amur tiger, consumption rates, global positioning system (GPS) collars, kill rates, Panthera tigris altaica,
Russian Far East, Siberian tiger, Sikhote-Alin Mountains, tiger
Ó 2013 American Society of Mammalogists
DOI: 10.1644/12-MAMM-A-209.1
Fewer than 3,500 wild tigers (Panthera tigris) remain in the
world (Walston et al. 2010) and all 6 remaining subspecies are
listed as endangered by the International Union for Conservation of Nature (IUCN). The Global Tiger Recovery Program, a
collaborative initiative endorsed by all 13 tiger range countries,
aims to double wild tiger numbers globally by 2022 (Global
Tiger Recovery Initiative 2010). Primary threats to tiger
persistence include habitat loss and fragmentation (Wikramanayake et al. 1998), depletion of prey species (Karanth and
Stith 1999; Miquelle et al. 1999b), direct killing of tigers for
traditional Chinese medicine (Nowell 2000), and retaliatory
killing after tiger–human conflicts (Miquelle et al. 2005a).
Approximately 10% of the world’s tigers inhabit the Russian
Far East, where a single metapopulation represents the vast
majority of Siberian, or Amur, tigers (P. t. altaica). In contrast
to other tiger subspecies, tiger range in the Russian Far East
consists of large contiguous forests with relatively low human
densities. Thus, the primary short-term threats to Amur tigers
are not necessarily habitat loss and fragmentation, but rather
declines in ungulate prey caused by unsustainable poaching
and hunting (Miquelle et al. 1999b) and direct tiger poaching
(Chapron et al. 2008).
Annual ungulate surveys from 1998 to 2009 documented a
steady decline in ungulate prey populations throughout Amur
tiger habitat (Miquelle et al. 2007). Hunting of large ungulates
is a traditional food source for residents and is legal in
www.mammalogy.org
0
0
Vol. 94, No. 4
JOURNAL OF MAMMALOGY
TABLE 1.—A review of published studies focusing on annual tiger (Panthera tigris altaica) kill rates on prey populations in Russia (based on
snow-tracking data) and Chitwan National Park, Nepal (based on very-high-frequency–telemetry data).
Kills/year
Days/kill
Study
Low
High
Range
Low
High
X̄
Range
Kovalchuk (1988)
Kucherenko (1977)
Kucherenko (1993)
Pikunov (1983)
Pikunov (1988)
Yudakov (1973)
Zhivotchenko (1979)
Sunquist (1981)a
Seidensticker (1976)a
This study
40
55
65
90
75
70
36
40
61
50.4
50
58
75
100
81
75
36
50
73
61.3
40–50
55–58
65–75
90–100
75–81
70–75
36–36
40–50
61–73
50.4–61.3
9.13
6.64
5.62
4.06
4.87
5.21
10.14
9.13
5.98
7.18
7.30
6.29
4.87
3.65
4.51
4.87
10.14
7.30
5.00
5.89
8.22
6.47
5.25
3.86
4.69
5.04
10.14
8.22
5.49
6.54
7.30–9.13
6.29–6.64
4.87–5.62
3.65–4.06
4.51–4.87
4.87–5.21
10.14–10.14
7.30–9.13
5.00–5.98
5.89–7.18
a
Study site located in Chitwan National Park, Nepal.
approximately 85% of the remaining 156,000 km2 of tiger
habitat in the Russian Far East (Miquelle et al. 1999a).
Unfortunately, conflict exists between Russian hunters and
tigers over a shared prey base. Because the majority of tiger
habitat in Russia is unprotected, and because Amur tigers
require large forested areas (Goodrich et al. 2008) with
sufficient ungulate prey and low human disturbance to survive
(Kerley et al. 2002) and reproduce (Kerley et al. 2003;
Goodrich et al. 2010), coexistence between tigers and people in
the multiple-use forests of the Russian Far East is a
conservation imperative (Miquelle et al. 2005a).
Legal ungulate harvest by human hunters is managed by the
Provincial Wildlife Departments of Primorye and Khabarovsk
by allocating a harvestable surplus of ungulates to humans
based on an estimated annual predation rate by the tiger
population (Miquelle et al. 2005a). Thus, one key to
minimizing conflict is the acquisition and application of
reliable scientific information about annual prey requirements
of Amur tigers. Unfortunately, data on kill rates and prey
requirements of wild tigers are difficult to obtain, particularly
during snow-free months in Amur tiger range or in tropical
portions of tiger range. To date, annual kill rates by Amur
tigers have been estimated by extrapolating winter kill rates
from intensive snow-tracking efforts (Yudakov and Nikolaev
1987; Pikunov 1988; World Wildlife Fund 2002). Recent
research has highlighted the dangers of extrapolating large
carnivore kill rates collected during winter without adjusting
for expected seasonal differences (Sand et al. 2008; Knopff et
al. 2010; Metz et al. 2012). Advances in global positioning
system (GPS) collars provide an alternative monitoring
technique that enables researchers to estimate kill rates yearround (Anderson and Lindzey 2003; Knopff et al. 2009;
Merrill et al. 2010). Anderson and Lindzey (2003) were the 1st
to use GPS collars to estimate large felid kill rates and their
approach has since been applied to a wide number of large
carnivore species (e.g., Webb et al. 2008; Cavalcanti and Gese
2010; Tambling et al. 2012). Nevertheless, GPS collars have
been deployed on Amur tigers only recently (Miller et al. 2011;
Rozhnov et al. 2011) and have not been used to estimate tiger
kill rates (Table 1).
Although kill rate is an important ecological parameter
influencing prey populations, ultimately it is consumption rate
that may determine tiger reproduction rates and population
dynamics (Sunquist et al. 1999). Metz et al. (2012) showed that
interpretations of seasonal predation varied significantly
depending on the metric used to quantify kill rates. For
example, kill rates of wolves (Canis lupus) in Yellowstone
National Park were higher in summer than in winter if looking
at kill rate as the number of animals killed per unit time but
lower in summer if looking at kill rate as the biomass acquired
per unit time (Metz et al. 2012). However, most studies of
carnivores do not actually estimate consumption rates through
behavioral observation; instead, they convert kill rates to
estimated consumption rates by adjusting for estimated losses
to scavengers, estimated live weights of prey from literature
values, edible portions of the prey species, and so on (Knopff et
al. 2010; Metz et al. 2012). Conversion of kill rates (number of
prey killed per unit time) to estimated consumption rates
(kilograms of prey consumed per unit time) allows for
comparisons between sexes and species (e.g., comparative
metabolic demands) or to sites with different prey species (and
sizes) available.
Here we use GPS data to estimate annual Amur tiger kill
rates and potential consumption rates in the Russian Far East.
We used clusters of locations obtained from GPS collars to
detect and examine putative tiger kill sites. Next, we developed
a logistic regression model to predict kill sites of ungulate prey
at clusters of locations. We then tested whether we could
predict body size of ungulate prey using a 2-step logistic
regression model (Knopff et al. 2009). Despite potential
seasonal differences in kill rates because of differential prey
size availability, actual intake or consumption rates may remain
the same because of seasonal variation in prey size (Sand et al.
2008; Metz et al. 2011). Therefore, we converted kill rates to
estimated consumption rates (kilograms per tiger per day) to
understand the energetic consequences of seasonal changes in
kill rates and prey sizes. Finally, we compared our GPS-based
kill rates to previous estimates of tiger kill rates from snow
tracking in Russia and very-high-frequency (VHF) tracking in
August 2013
MILLER ET AL.—AMUR TIGER KILL AND CONSUMPTION RATES
FIG. 1.—Our study was focused in and around the 4,000-km2
Sikhote-Alin Biosphere Zapovednik, Russian Far East, from 2009 to
2012.
Nepal, as well as to other GPS-based kill-rate estimates from
other large felids.
MATERIALS
AND
METHODS
Study area.—We conducted our research in and around the
4,000-km2 Sikhote-Alin Biosphere Zapovednik (or Reserve).
Founded in 1935, the Reserve is an IUCN Category I protected
area near the village of Terney, Primorskii Krai (province), in
the Russian Far East (Fig. 1). Access to the Reserve is strictly
limited to Reserve staff and visiting scientists. Inside the
Reserve, hunting is illegal and poaching is relatively low,
whereas prey populations outside of the Reserve are exposed to
legal hunting and high poaching rates (Miquelle et al. 2005b).
Within the Reserve, the Sikhote-Alin Mountains parallel the
Sea of Japan with elevations reaching 1,600 m, but most peaks
are , 1,200 m. The Reserve occurs in the Far Eastern
temperate climatic zone and is characterized by strong
seasonality with dry, cold winters (X̄ ¼ 12.98C, January in
Terney), moderate snowfall (X̄ ¼ 1,190 mm snow in Terney per
winter), warm and humid summers (X̄ ¼ 158C, July in Terney),
and average annual precipitation of 760 mm (Gromyko 2010).
0
Dominant vegetation communities within the Reserve include
oak (Quercus mongolica) forests along the coast and mixed
conifer–deciduous forests at higher elevations including
Korean pine (Pinus koraiensis), larch (Larix komarovii),
birch (Betula spp.), and mixed forests of spruce (Picea
ajanensis) and fir (Abies nephrolepis—Vasiliev and Fliagina
2006). The key tiger prey species in the Reserve include red
deer (Cervus elaphus), wild boar (Sus scrofa), sika deer
(Cervus nippon), and roe deer (Capreolus pygargus—Miquelle
et al. 1996, 2010b). Amur tigers in the Reserve also
opportunistically prey on moose (Alces alces), musk deer
(Moschus moschiferus), ghoral (Naemorhedus caudatus),
brown bear (Ursus arctos), Asiatic black bear (U.
thibetanus), wolf, red fox (Vulpes vulpes), raccoon dog
(Nyctereutes procyonoides), badger (Meles leucurus), lynx
(Lynx lynx), and domestic dog (Canis lupus familiaris—
Miquelle et al. 1996).
Predicting tiger kill and consumption rates with GPS
data.—We deployed GPS collars on tigers captured in and
around the Reserve from 2009 to 2012 using modified Aldrich
foot snares (Goodrich et al. 2001). Tigers were anesthetized
with Zoletil (Lewis and Goodrich 2009) and fitted with
VECTRONIC (Berlin, Germany) or LOTEK (Newmarket,
Ontario, Canada) satellite GPS collars that allowed for realtime monitoring within days or 1–2 weeks of predation sites.
Capture and handling of tigers followed guidelines of the
American Society of Mammalogists (Sikes et al. 2011), and
protocols were approved by the University of Montana
Institutional Animal Care and Use Committee (UM IACUC
AUP 043-09) and the Wildlife Conservation Society Global
Health unit. We combined GPS data collection with stratified
sampling of potential kill sites to estimate tiger kill rates as the
number of days between kills (days per kill per tiger) and in
terms of potential biomass consumption rates (kilograms per
tiger per day). We stratified potential kill sites for investigation
according to the relative probability (i.e., high or low) that a
GPS location was a kill site by adapting previously developed
methods for large cats (Knopff et al. 2009). To estimate the
number of kills, we used GPS location data to detect clusters of
locations in close spatial and temporal proximity that
represented potential kill sites (Anderson and Lindzey 2003).
Previous research on mountain lions (Puma concolor)
indicated that 95% of kill sites were correctly identified at a
fix rate of 1 location/4 h (Anderson and Lindzey 2003). We
programmed collars to obtain locations at intervals of 90, 180,
or 360 min. After uploading GPS data, we used a Python script
(Python Software Foundation, Hampton, New Hampshire)
developed by Knopff et al. (2009) to identify potential kill sites
as clusters of 2 or more locations within 100 m and 48 h of
each other. We located kill sites by physically searching 50 m
or more around each location in a cluster. During winter, we
located kill sites by downloading GPS data from collars and
snow tracking GPS-collared tigers to clusters in the field.
During snow-free months, we relied on GPS data downloads
and cluster searches to locate kill sites. We attempted to search
putative kill sites for prey remains after 1–2 weeks of receiving
0
JOURNAL OF MAMMALOGY
location data to avoid displacing tigers from kills or losing
information to decomposition or scavenging of the carcass
(Sand et al. 2008; Webb et al. 2008). We searched most
(88.5%) of the largest clusters ( 24 h from 1st location in the
cluster until the last) and many smaller clusters to determine
which clusters contained kill sites, but also searched a subset (n
¼ 518) of nonclustered GPS locations to verify that our
sampling technique did not underestimate potential kill sites.
We also collected data during an intensive sampling period
where we searched every location (clustered and nonclustered)
from an individual tiger during a 2-week period in the summer
to verify presence or absence of small-prey remains. We
opportunistically snow tracked 134 km between consecutive
GPS locations of 2 different tigers, which ensured a near 100%
kill-site recovery rate along the route, regardless of prey size.
We used multiple logistic regression (Hosmer and Lemeshow 2000) to model the presence or absence of a kill at GPS
clusters. We measured 6 potential spatiotemporal predictor
variables for each GPS cluster: hours: the total number of hours
between the 1st and last locations in the cluster; days: the
number of 24-h periods when at least 1 fix was obtained within
the cluster; average distance: the average distance away from
the cluster center that all points in the cluster were located;
radius: the difference between the cluster center and the farthest
clustered point away; multiday binary: a binary coding of days
on the cluster that separated clusters into those with locations
across multiple 24-h periods and those with all locations within
a single 24-h period (e.g., Knopff et al. 2009); and percent
fidelity: the percentage of locations over the duration of the
cluster that fell within the cluster. We estimated the
explanatory power of these variables using logistic regression
to predict the presence (1) or absence (0) of a kill (Pr(Kill))
following:
PrðKillÞ ¼
expðb0 þb1 *X1 þb2 *X2 þb3 *X3 þ...b8 *X8 Þ
;
1 þ expðb0 þb1 *X1 þb2 *X2 þb3 *X3 þ...b8 *X8 Þ
ð1Þ
where b0 is the intercept, and bs are the coefficients of the
effects of the covariates, Xi, on Pr(Kill). We excluded
explanatory variables that were correlated at r 0.7 (Webb
et al. 2008). We developed a set of a priori candidate models
using combinations of noncollinear predictor variables, fit them
to the data, and assessed model support with Akaike’s
information criteria (AIC—Burnham and Anderson 2002).
We summed AIC weights (Rwi) from the top models to rank
support among predictor variables influencing the probability a
cluster contained a kill site. To correct for potential missed kills
in our full cluster data set, we then used predicted values from
our top model to estimate the probability of kills at clusters we
were unable to field sample (Knopff et al. 2009). We
conducted all analyses using Stata 11.0 (Stata Corp., College
Station, Texas).
To distinguish between large- and small-bodied prey species
based on GPS data, we used a 2nd multiple logistic regression
analysis to model the effects of the same 6 cluster parameters
on prey size (e.g., Knopff et al. 2009). For consistency within
the tiger literature, we followed Chundawat et al. (1999) in
Vol. 94, No. 4
using a 40-kg cut point to assign prey species into small (0) and
large (1) categories. For each cluster in our full data set we then
estimated the probability a cluster contained a kill (i.e.,
equation 1 above), and 2nd, the probability that predicted kills
were small or large.
We used sensitivity and specificity curves to classify
predictions from the top regression models differentiating
clusters as kill sites from nonkill sites and small-prey kill sites
from large-prey kill sites (Hosmer and Lemeshow 2000;
Knopff et al. 2009). The cut point for the probability of a
cluster being a kill has a direct bearing on model performance
and estimated kill rates (Zimmermann et al. 2007; Webb et al.
2008; Knopff et al. 2009). A cut point that maximizes
sensitivity will correctly classify most kill sites but may
incorrectly classify a high proportion of nonkill sites, thereby
overestimating the predicted kill rate. Conversely, a cut point
that maximizes specificity will correctly classify most nonkills
but may incorrectly classify many kill sites as nonkills, thereby
underestimating the predicted kill rate. We selected a cut-point
value that maximized overall prediction success to determine if
a cluster contained a probable kill site in the 1st model or a
probable large-prey kill site in the 2nd model (Hosmer and
Lemeshow 2000; Liu et al. 2005).
We estimated the kill rate as the sum of predicted kills
divided by the number of days of continuous monitoring. We
calculated kill-rate variance using a design-based ratio
estimator with individual tigers as the sample unit (Thompson
2002; Hebblewhite et al. 2003). We estimated potential
consumption rates by converting our kill-rate estimates into
potential prey biomass (kg) consumed per tiger per day. To do
this, we multiplied the predicted kill rates by the proportion of
each prey species in our field-verified sample and the
corresponding average prey species weights across different
sex and age classes. The average weights of primary prey
species in the Russian Far East have been reported for all sex
and age classes (Bromley and Kucherenko 1983; Danilkin
1999). Because of variation in digestibility and our lack of
ability to conduct feeding trials, we relied on literature where
such procedures have been studied. For instance, the edible
portion of elk (Cervus elaphus) was estimated to be 68%
(Wilmers et al. 2003) and white-tailed deer (Odocoileus
virginianus) was estimated to be about 79% (Ackerman et al.
1986). Using these estimates, we assumed 68% of a large-prey
carcass was edible and 79% of a small-prey carcass was edible
biomass. Tigers that are not disturbed by humans rarely leave
edible portions of a carcass (Kerley et al. 2002), but Yudakov
and Nikolaev (1987) estimated that 15% of each tiger kill was
lost to scavengers. Because human disturbance in the
backcountry of the Reserve is limited, we assumed tigers did
not abandon kill sites, consumed all edible portions, and lost
15% of each prey item to scavengers. As with kill rates, we
used a design-based ratio estimator to calculate variance in
potential consumption rates (Thompson 2002; Hebblewhite et
al. 2003).
Finally, following Cavalcanti and Gese (2010), we tested the
relationship between the interkill time interval and the size of
August 2013
0
MILLER ET AL.—AMUR TIGER KILL AND CONSUMPTION RATES
TABLE 2.—Summary of data used during analyses of Amur tiger (Panthera tigris altaica) kill rates (days kill1 tiger1) and potential
consumption rates (kg consumed day1 tiger1; CR) on and near Sikhote-Alin Biosphere Zapovednik, Russia, from 2009 to 2012.
Tiger indentification
Pt99
Pt100
Pt114
Total
X̄
SD
Ratio estimator
Ratio SD
Sex
Female
Male
Female
3
No. days
monitored
No. locations
Fix %
No. clusters
searched
No. kills
420
99
311
830
276.7
163.23
2,988
1,529
4,644
9,161
3,053.7
1,558.54
90.4
96.6
96.4
94.4
94.5
3.52
169
48
161
378
126.0
67.67
47
14
50
111
37.0
19.97
predicted kills using a 1-tailed t-test to test whether satiation
following larger kills might influence kill rates. To evaluate the
interkill interval of predicted kills, we compared the time from
the 1st location in a predicted kill site to the 1st location in the
following predicted kill site with the predicted size (large or
small) of the kill.
RESULTS
Predicting tiger kill and consumption rates with GPS
data.—From 2009 to 2012, we captured and GPS-collared 3
adult females, 2 adult males, and 1 subadult female tiger
(subadults being individuals no longer associating with their
mother but not yet reproducing), but we restricted analyses to 3
adults with sufficient data to estimate kill rates (Table 2). These
3 tigers were each monitored 99–420 days, with a combined
total of 830 tiger-days (Table 2). We obtained 1,529–4,644
locations from each tiger, with a total of 9,161 locations and a
fix success rate of 94.5% (Table 2).
As our focus was predicting kill rates and potential
consumption rates of healthy adult tigers, we screened out
locations when tigers were known or believed to be unhealthy
(see ‘‘Discussion’’ for more details). Pt100 and Pt114 both
lived in and around the Reserve, but Pt99 lived exclusively in
unprotected, multiple-use forests. We estimated 982 unique
clusters representing potential kill sites and investigated 378
clusters (range, 48–169 clusters per tiger or 36.1–41.6% of
Observed
days/kill
Predicted
days/kill
Observed CR
Predicted CR
8.94
7.07
6.22
6.77
5.21
6.76
4.79
7.20
7.63
8.62
11.20
8.64
7.41
1.389
7.48
0.727
6.25
0.899
6.54
0.211
6.54
1.534
6.14
0.089
9.49
1.488
8.93
0.023
total clusters from each tiger), resulting in 109 observed kills at
clusters (range, 14–50 per tiger; Table 2). Two additional kills
(both were badgers) were located at single locations during our
investigation of a subset of nonclustered GPS locations (n ¼
518). Of the total observed kills, 27.9% were wild boar, 24.3%
were red deer, and 23.4% were roe deer (Table 3). Among
known wild boar kills, 25.8% were adults, 67.7% were
juveniles and piglets, and 6.5% could not be accurately
classified into an age class. Red deer kills consisted of 70.4%
adults and 29.6% juveniles and calves. Among roe deer kills,
53.9% were adults, 26.9% were juveniles, and 19.2% could not
be accurately classified. Juvenile ungulates comprised 18
(50.0%) of 36 observed summer large ungulate kills and 17
(51.5%) of 33 winter kills. Overall, wild ungulate species
represented 90.1% of all tiger kills, with nonungulate or
domestic prey comprising the remaining kills (Table 3).
Our best logistic regression model for differentiating clusters
that contained tiger kills from nonkill clusters included hours at
the site and percent fidelity to the site (Table 4). The top model
showed that the probability a cluster contained a kill increased
as a tiger spent more time at a site and as fidelity to the site
increased (P 0.005; Table 5). Covariates were ranked in the
following order based on summed variable importance weights
(Rwi) of the top 8 models: 1st, percent fidelity to the site Rwi ¼
1.000; 2nd, hours at the site Rwi ¼ 0.965; 3rd, radius of cluster
Rwi ¼ 0.252; 4th, average distance from each location to the
cluster center Rwi ¼ 0.198; 5th, clusters that contained
TABLE 3.—Prey species located at Amur tiger (Panthera tigris altaica) kill sites identified at single global positioning system (GPS) locations or
from logistic regression–directed cluster sampling of GPS-collared tigers in the Sikhote-Alin Mountains, Russian Far East, 2009–2012.
Prey species
No. kills located
% of total kills
% biomassa
Wild boar (Sus scrofa)
Red deer (Cervus elaphus)
Roe deer (Capreolus pygargus)
Sika deer (Cervus nippon)
Musk deer (Moschus moschiferus)
Brown bear (Ursus arctos)
Asiatic black bear (U. thibetanus)
Feral dog (Canis lupus familiaris)
Badger (Meles leucurus)
Cattle (Bos taurus)
Total
31
27
26
15
1
1
1
3
5
1
111
27.9
24.3
23.4
13.5
0.9
0.9
0.9
2.7
4.5
0.9
100
26.9
43.6
12.6
12.9
0.2
1.6
0.9
0.5
0.7
0.2
100
Kills/day (SE)
0.037
0.033
0.031
0.018
0.001
0.001
0.001
0.004
0.006
0.001
0.134
(0.004)
(0.004)
(0.003)
(0.008)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.001)
(0.009)
a
Percent biomass was calculated by multiplying each prey item by weight estimates from published data for the corresponding age class and then dividing by overall consumption
estimates.
0
Vol. 94, No. 4
JOURNAL OF MAMMALOGY
TABLE 4.—The top 8 multiple logistic regression models for predicting Amur tiger (Panthera tigris altaica) kill sites in the Russian Far East
from clusters not associated with a kill site. Avg Dist ¼ average distance.
Model description
n
K
Log likelihood
DAICc
AIC weights
Evidence ratio—
compared to best model
Hours þ % Fidelity
Hours þ Radius þ % Fidelity
Hours þ % Fidelity þ Avg Dist
MDB1 þ % Fidelity
MDB þ Radius þ % Fidelity
MDB þ Avg Dist þ % Fidelity
Days þ % Fidelity
Days þ Radius þ % Fidelity
380
380
380
380
380
380
380
380
3
4
4
3
4
4
3
4
140.801
140.582
140.799
144.347
143.527
144.305
147.660
146.713
0.000
1.605
2.038
7.091
7.494
9.050
13.717
13.866
0.533
0.239
0.193
0.015
0.013
0.006
0.001
0.001
1.000
0.448
0.361
0.029
0.024
0.011
0.001
0.001
a
MDB ¼ Multiday binary: a binary coding of hours on the cluster that separated clusters into those with locations across multiple 24-h periods and those with all locations within a single
24-h period.
locations from multiple days (Multi Day Bin) Rwi ¼ 0.034; and
6th, number of 24-h periods with at least 1 location at the site
Rwi ¼ 0.001. We used only the top model (AIC weight ¼
0.533) because model averaging would have included collinear
variables, which included days, hours, and multiday binary, as
well as average distance and radius. The top model for
distinguishing kill sites from nonkill sites fit the data well
(likelihood ratio chi-square score of 177.46 [P-value ,
0.0001], pseudo R2 ¼ 0.39, and receiver operating characteristic [ROC] score of 0.894). The maximized probability cutoff
for which we considered a cluster a probable kill site was 0.23,
which corresponded to an overall classification success of
86.3%.
The number of hours at a site was the only predictive
variable in our top model for discriminating small prey from
large prey (P 0.005; Table 6). Our top model suggested that
the probability a kill was a large-prey item increased with
increasing hours spent at the site (Table 5). Despite a low AIC
weight, we chose to use only the top model (AIC weight ¼
0.232), instead of multimodel inference, because model
averaging would have included collinear variables. Considering summed variable importance weights (Rwi) of the top 10
models, covariates were ranked in the following order: 1st,
hours spent at the site Rwi ¼ 0.681; 2nd, radius of cluster Rwi ¼
0.308; 3rd, days at the site Rwi ¼ 0.275; 4th, percent fidelity to
the site Rwi ¼ 0.243; and 5th, average distance from each
location to the cluster center Rwi ¼ 0.165. Our top model for
predicting small-prey from large-prey kill sites fit the data well
(likelihood ratio chi-square score of 27.39 [P-value , 0.0001],
pseudo R2 ¼ 0.20, and ROC score of 0.801). The optimal
probability cutoff for which we considered a cluster a largeprey kill site was 0.72, which corresponds to an overall
classification success of 71.2%.
Our logistic regression model predicted slightly higher kill
rates than those estimated from only those kills observed in the
field, mostly due to 8 kills predicted by the model that were not
investigated during field sampling. The average kill rate
estimated only on the basis of observed kills was 1 kill every
7.48 days (95% confidence interval [95% CI] 5.27–9.69 days;
ratio-estimator SE ¼ 0.51), or 0.13 kills/day (95% CI 0.094–
0.173 kills/day; SE ¼ 0.009), resulting in an average of 48.8
kills/year (95% CI 34.4–63.3 kills/year; SE ¼ 3.36). Predicted
kill rates from our top logistic regression model were slightly
higher—an average of 1 kill every 6.54 days (95% CI 5.89–
7.18 days; SE ¼ 0.149), or 0.153 kills tiger1 day1 (95% CI
0.138–0.168 kills tiger1 day1; SE ¼ 0.0035). Using these
predicted kill rates, the annual kill rate was 55.8 prey killed
tiger1 year1 (95% CI 50.4–61.3 prey killed tiger1 year1; SE
¼ 1.28). Using these predicted kill-rate estimates and known
composition of sizes and proportions of observed kills, the
potential consumption rate from our top logistic regression
model was 8.93 kg day1 tiger1 (95% CI 8.83–9.03 kg day1
tiger1; SE ¼ 0.023), or an average of 3,260.6 kg year1 tiger1
(95% CI 3,224.7–3,296.5 kg year1 tiger1; SE ¼ 8.35). The
observed potential consumption rates from all monitored tigers
averaged 6.14 kg day1 tiger1 (95% CI 5.76–6.52 kg day1
tiger1; SE ¼ 0.09), with total biomass consumed composed of
26.9% boar, 43.6% red deer, 12.9% sika deer, 12.6% roe deer,
and 4.0% of all other prey items (Table 3).
Limited sample size restricted our ability to conduct a
rigorous comparison of summer versus winter kill rates. We
found both predicted consumption rates (7.89 kg day1 tiger1
in summer versus 10.3 kg day1 tiger1 in winter) and kill rates
(7.4 days kill1 tiger1 in summer versus 5.7 days kill1 tiger1
in winter) were lower during summer months. Additionally, we
observed an increase in large-bodied prey killed during the
TABLE 5.—Beta coefficients from the top multiple logistic regression models used to predict Amur tiger (Panthera tigris altaica) kill sites from
nonkill sites at clusters of locations, and to predict Amur tiger small-prey kill sites from large-prey kill sites in the Russian Far East.
Pr (kill, no kill)
Pr (large kill, small kill)
Covariate
Coefficient
SE
P-value
Coefficient
SE
P-value
Constant (b0)
Hours
% fidelity
7.03
0.08
4.49
1.128
0.010
1.054
, 0.0005
, 0.0005
, 0.0005
0.91
0.03
0.410
0.009
0.027
, 0.0005
August 2013
0
MILLER ET AL.—AMUR TIGER KILL AND CONSUMPTION RATES
TABLE 6.—The top 10 multiple logistic regression models for predicting Amur tiger (Panthera tigris altaica) small-prey kill sites from largeprey kill sites in the Russian Far East. Avg Dist ¼ average distance.
Model description
n
K
Log likelihood
DAICc
AIC weights
Evidence ratio—
compared to best model
Hours
Hours þ Radius
Days
Hours þ % Fidelity
Hours þ Avg Dist
Days þ Radius
Hours þ Radius þ % Fidelity
Days þ Avg Dist
Days þ % Fidelity
Hours þ % Fidelity þ Avg Dist
111
111
111
111
111
111
111
111
111
111
2
3
2
3
3
3
4
3
3
4
55.491
54.814
56.233
55.262
55.432
55.474
54.613
56.124
56.219
55.210
0.000
0.758
1.485
1.656
1.996
2.078
2.509
3.379
3.570
3.705
0.232
0.159
0.111
0.102
0.086
0.082
0.066
0.043
0.039
0.036
1.000
0.684
0.476
0.437
0.369
0.354
0.285
0.185
0.168
0.157
winter months (64.2% of kills in summer versus 75% in
winter).
Finally, we found the average interkill interval after
predicted kills of small prey (5.75 days; SE ¼ 0.58) was
shorter than the interkill interval after predicted kills of large
prey (8.12 days; SE ¼ 0.58; 1-tailed t-test; P ¼ 0.002),
suggesting that kill rates may be influenced by satiation or
handling time of prey.
DISCUSSION
The annual tiger kill rates and potential consumption rates
predicted from our top logistic regression model (6.54 days/kill
and 8.93 kg/day for adult tigers) are relatively high compared
to most estimates from other published studies in Russia and
Chitwan National Park, Nepal (Table 1). Only 1 previous study
in the Russian Far East reported a lower kill-rate estimate
(days/kill) than ours (Zhivotchenko 1979). In radiotelemetry
studies based in Chitwan National Park, a solitary female tiger
was reported to make a kill every 7.3–9.1 days (Sunquist 1981)
and a female with two 6- to 10-month-old dependent cubs
made a kill every 5–6 days (Table 1; Seidensticker 1976). Killrate estimates based on snow tracking in Russia ranged widely
from 3.86 to 10.14 days/kill, but the overall average, 6.2 days/
kill (95% CI 4.0–8.5 days/kill; SE ¼ 0.91; Table 1) was very
close to our estimate. Potential annual tiger consumption rates
in and around the Reserve (8.93 kg/day) were slightly higher
than consumption rate estimates in both Chitwan National
Park, Nepal (males 6–7 kg/day and females 5–6 kg/day
[Sunquist 1981]) and Kanha National Park, India (5–7 kg
day1 tiger1 [Schaller 1967]). Previous estimates of potential
consumption rates based on snow tracking in the Russian Far
East (5–15 kg day1 tiger1 [Pikunov 1988] and 7.2 kg/day
[Yudakov and Nikolaev 1987]) resulted in overlapping
estimates with our GPS-based estimates. Potential consumption
rate estimates of captive tigers (males 6 kg/day and females 3–
4 kg/day) were lower than our estimates from the field (Yudin
1990).
Our estimates are higher than most previously reported
results, likely due to both methodological and ecological
differences. Intensive snow-tracking studies of individual
tigers, such of those of Yudakov and Nikolaev (1987), should
provide the most precise data on kill rates, because missing
kills is unlikely when individuals were tracked continuously
over extended periods. However, sample sizes from such
intensive tracking tend to be low (e.g., 21 kills for Yudakov
and Nikolaev [1987]), potentially reducing accuracy, and it is
possible to push tigers from kills while tracking, causing them
to eat less from each kill and to kill more frequently (Kerley et
al. 2002). This may partially explain some of the previously
published higher kill-rate estimates associated with snow
tracking. Our intensive field sampling, guided by stratifying
sampling of GPS location clusters, still occasionally missed kill
sites predicted from the logistic regression model at clusters we
failed to prioritize for field investigation, thereby underestimating kill rates using observed kills alone. We were, however,
able to estimate our success rates for finding kills and hence
correct our empirical kill-rate estimates. We also may have
missed kills of small body size (2 badgers detected at single
GPS locations), although such kills are of negligible consequence in terms of potential biomass consumption (Bacon et al.
2011). We believe our combination of GPS cluster searching
and snow tracking should provide high kill-site detection rates
and with the larger sample sizes possible with this approach,
should result in more accurate estimates of both kill rates and
potential consumption rates.
Variation in the body sizes of prey killed also could
contribute to discrepancies between kill-rate estimates and
potential consumption-rate estimates. Cavalcanti and Gese
(2010) found jaguar kill rates decreased and the amount of time
between kills increased with increasing body size of prey.
Similarly, seasonal differences in wolf kill rates have been
driven by increases in juvenile kills during summer (Sand et al.
2008). We also found that tigers killing smaller prey items
made kills more often, as might be expected if tigers are trying
to maintain some minimum consumption rate. These differences in kill rates, in which a segment of a prey population is
being targeted (e.g. adults versus juveniles), will no doubt
impact prey population dynamics differently. Similar to Metz
et al. (2012), our results indicate that tigers are preying on more
juvenile ungulates and smaller, nonungulate prey during
summer. The increase in predicted consumption rates we
observed in winter corresponds well with the theory that
biomass acquisition should be greater in the winter due to the
0
JOURNAL OF MAMMALOGY
Vol. 94, No. 4
FIG. 2.—Predicted probability of Amur tiger (Panthera tigris altaica) kill sites as a function of hours on a cluster (dashed line) and then
predicting large kills (. 40 kg) versus small kills as a function of number of hours on a cluster (solid line). The individual markers represent the
predicted probability a cluster contains a kill site at confirmed small- and large-prey kill sites from investigated clusters in the Russian Far East,
from 2009 to 2012.
additional energetic requirements from thermoregulatory
demands (Mautz and Pekins 1989).
Our diet composition results differed slightly from previous
research on Amur tigers, which showed red deer and wild boar
comprised 63–92% of all kills from 6 sites across tiger range in
Russia (Miquelle et al. 2010b). In contrast, only 52.3% of our
confirmed kills were red deer or wild boar, and only 48.8% of
predicted kills were classified as large prey. These differences
could arise from a number of reasons, not only our limited
sample size of tigers. First, much previous research was based
in and around the Reserve, an area known for harboring
healthy red deer and wild boar populations, whereas much of
our data come from 1 tiger living outside protected areas,
where roe deer were more abundant. For example, kills made
by Pt99 represented 42.3% of our total prey sample and were
all located outside of protected areas. Only 48.9% of Pt99’s
kills were large-bodied prey, whereas 81.3% of kills by other
tigers were large-bodied. Second, methodologically, our yearround GPS methods are more likely to locate small-prey kill
sites (i.e., roe deer) compared to the very-high-frequency
radiotelemetry techniques used by Miquelle et al. (2010b) and
because most previous work occurred during winter when
many small-prey species are hibernating. Finally, recent
declines in red deer and wild boar populations in our study
area due to increased poaching rates outside protected areas
(i.e., up 50% in the last decade [Miquelle et al. 2010a]), could
have caused significant diet shifts. Our estimates of tiger kill
rates and potential consumption rates are comparable to those
of previous studies and the 1st rigorous year-round estimates
for tigers derived from GPS collars in the scientific literature. A
larger sample of Amur tigers, both inside and outside of
protected areas, would improve the precision of our estimates.
We found the number of hours present and high fidelity to a
site were the most important factors in determining if a GPS
cluster contained a kill site (Figs. 2 and 3). Similarly, both
Anderson and Lindzey (2003) and Knopff et al. (2009) found
the number of nights and the amount time, respectively, to best
predict mountain lion kills at GPS clusters. Webb et al. (2008)
found that the 2 most important variables used to distinguish
wolf kills were the number of days spent within 100 m of a
cluster and the number of GPS locations (i.e., hours) within
100 m of the cluster center. Clearly, identifying long periods of
localized activities can be a simple method of locating largeprey kill sites for large predators (Miller et al. 2010). Several
recent studies have used either multinomial logistic regression
or sequential logistic regression to predict kill rates of specific
prey species (Knopff et al. 2009) or different prey sizes (Webb
et al. 2008). We found the total number of hours spent at a
cluster to be the most important factor in determining if a
cluster contained a large prey. Although we were unable to
predict specific prey species composition at kill sites from GPS
data, our model proved to be very good at predicting large-prey
kill sites from small-prey kill sites. Such a technique also might
be useful for systems dominated by a single prey species where
adults and calves could be easily differentiated.
Our results have several limitations, the most obvious being
a limited sample size of collared tigers. To restrict our analyses
to data from healthy adult tigers, we excluded data from these
analyses to avoid concerns related to disease, injury, subadults
traveling with their mother, and small data sets related to collar
malfunctions. The 3 tigers we used were therefore quite
representative of the adult tiger population. Regardless of small
sample size, our study is among the 1st and most successful
studies of tiger predator–prey relationships with GPS collars in
the wild. For example, in a dissertation by Barlow (2009),
information from 2 GPS-collared tigers provided important
gains in our understanding of tiger predation. The only other
published study, also from the Russian Far East, used data from
August 2013
MILLER ET AL.—AMUR TIGER KILL AND CONSUMPTION RATES
0
FIG. 3.—Predicted probability of Amur tiger (Panthera tigris altaica) kill sites as a function of percent fidelity to site at multiple temporal scales
in the Russian Far East, from 2009 to 2012.
only 1 GPS-collared adult female tiger (Rozhnov et al. 2011).
This work on GPS-based predator–prey relationships complements some of the pioneering work on tiger predation by
Sunquist (1981) and Seidensticker (1976) that provided the 1st
published kill rates focusing on 1 tigress each. More broadly,
even the most comprehensive demographic study of Amur
tigers in the wild (Kerley et al. 2003) used data from 8 adult
female tigers and 7 cubs. Clearly, one of the challenges facing
all empirical tiger ecological studies is the challenge of small
sample sizes, and yet, the original studies and our GPS-based
efforts provide convergent insights into tiger predation ecology
that will help conservation. More data are still needed to better
differentiate potential consumption rates of sex–age classes of
tigers and seasonality of kill and consumption rates, but our
study clearly demonstrates the utility of using GPS technology
to understand tiger predator–prey requirements in the field.
Hunters are key stakeholders in tiger conservation, with
more than 60,000 registered hunters on multiple-use lands in
the Russian Far East. Managers of private wildlife management
concessions are largely responsible for managing hunting,
controlling poaching, and conducting surveys of game species
on leased hunting territories, which encompass about 85% of
Amur tiger habitat. Our results suggest that annual kill rates of
Amur tigers may be slightly higher than previously reported
estimates based on extrapolated winter estimates. Therefore,
extrapolating historic snow-tracking–based kill-rate estimates
over the entire year may lead to an underestimate of annual
harvest of ungulates by tigers and a subsequent overestimate of
the surplus available for human harvest. If annual harvest of
ungulates by tigers is estimated conservatively, sustainable
hunting continues, and yet prey populations continue to
decline, poaching is the likely culprit. Our results show
promise for estimating kill rates and prey requirements of tigers
in southern Asia where snow tracking is not possible. Given
that most published estimates of kill rates of tigers are from
Amur tigers (Table 1), GPS collars may provide a crucial tool
to better understand prey requirements to conserve tiger
populations across the species’ range.
ACKNOWLEDGMENTS
We thank A. Astafiev, Director, and Y. Pimenova, Assistant
Director, of the Sikhote-Alin Zapovednik for their ongoing support of
our research. The Siberian Tiger Project staff, particularly N. Rybin,
V. Melnikov, and E. Gishko, helped collect data in the field. H.
Robinson, J. Stetz, M. Metz, and K. Knopff all provided expertise
during data analyses, and comments from J. Berger, M. Mitchell, and
2 anonymous reviewers substantially improved the manuscript.
Funding was provided by the Mohamed bin Zayed Species
Conservation Fund, Panthera’s Kaplan Graduate Award Program,
Liz Claiborne and Art Ortenburg Foundation, Save the Tiger Fund,
United States Fish and Wildlife Service Tiger Rhino Conservation
Fund, University of Montana, and the Wildlife Conservation Society.
LITERATURE CITED
ACKERMAN, B. B., F. G. LINDZEY, AND T. P. HEMKER. 1986. Predictive
energetics model for cougars. Pp. 333–352 in Cats of the world:
biology, conservation, and management (S. D. Miller and D. D.
Everett, eds.). National Wildlife Federation, Washington, D.C.
0
JOURNAL OF MAMMALOGY
ANDERSON, C. R., AND F. G. LINDZEY. 2003. Estimating cougar
predation rates from GPS location clusters. Journal of Wildlife
Management 67:307–316.
BACON, M. M., G. M. BECIC, M. T. EPP, AND M. S. BOYCE. 2011. Do
GPS clusters really work? Carnivore diet from scat analysis and
GPS telemetry methods. Wildlife Society Bulletin 35:409–415.
BARLOW, A. C. D. 2009. The Sundarbans tiger: adaptation, population
status, and conflict management. Ph.D. dissertation, University of
Minnesota, Saint Paul.
BROMLEY, G. F., AND S. P. KUCHERENKO. 1983. Ungulates of the
southern Far East USSR. Nauka (Science) Press, Moscow, Russia.
BURNHAM, K. P., AND D. R. ANDERSON. 2002. Model selection and
multimodel inference: a practical information-theoretic approach.
2nd ed. Springer-Verlag, New York.
CAVALCANTI, S. M. C., AND E. M. GESE. 2010. Kill rates and predation
patterns of jaguars (Panthera onca) in the southern Pantanal, Brazil.
Journal of Mammalogy 91:722–736.
CHAPRON, G., D. MIQUELLE, A. LAMBERT, J. M. GOODRICH, S. LEGENDRE,
AND J. CLOBERT. 2008. The impact on tigers of poaching versus prey
depletion. Journal of Applied Ecology 45:1667–1674.
CHUNDAWAT, R. S., N. GOGATE, AND A. J. T. JOHNSINGH. 1999. Tigers in
Panna: preliminary results from an Indian tropical dry forest. Pp.
123–129 in Riding the tiger: meeting the needs of people and
wildlife in Asia (J. Seidensticker, S. Christie, and P. Jackson, eds.).
Cambridge University Press, Cambridge, United Kingdom.
DANILKIN, A. A. 1999. Mammals of Russia and adjacent regions: deer
(Cervidae). GEOS, Moscow, Russia.
GLOBAL TIGER RECOVERY INITIATIVE. 2010. Global Tiger Recovery
Program. Global Tiger Initiative, Secretariat, The World Bank,
Washinton, D.C.
GOODRICH, J. M., ET AL. 2001. Capture and chemical anesthesia of
Amur (Siberian) tigers. Wildlife Society Bulletin 29:533–542.
GOODRICH, J. M., ET AL. 2008. Survival rates and causes of mortality of
Amur tigers on and near the Sikhote-Alin Biosphere Zapovednik.
Journal of Zoology (London) 276:323–329.
GOODRICH, J. M., D. G. MIQUELLE, E. N. SMIRNOV, L. L. KERLEY, H. B.
QUIGLEY, AND M. G. HORNOCKER. 2010. Spatial structure of Amur
(Siberian) tigers (Panthera tigris altaica) on Sikhote-Alin Biosphere Zapovednik, Russia. Journal of Mammalogy 91:737–748.
GROMYKO, M. N. 2010. Physical and geographic conditions of the
study area. Pp. 54–78 in Fires and their influence on the natural
ecosystems of the central Sikhote-Alin (B. S. Petropavlovsky and
A. A. Astafiev, eds.). Dalnauka, Vladivostok, Russia.
HEBBLEWHITE, M., P. C. PAQUET, D. H. PLETSCHER, R. B. LESSARD, AND
C. J. CALLAGHAM. 2003. Development and application of a ratio
estimator to estimate wolf kill rates and variance in a multiple-prey
system. Wildlife Society Bulletin 31:933–946.
HOSMER, D. W., AND S. LEMESHOW. 2000. Applied logistic regression.
Wiley-Interscience, New York.
KARANTH, K. U., AND B. M. STITH. 1999. Prey depletion as a critical
determinant of tiger population viability. Pp. 100–113 in Riding the
tiger: meeting the needs of people and wildlife in Asia (J.
Seidensticker, S. Christie, and P. Jackson, eds.). Cambridge
University Press, Cambridge, United Kingdom.
KERLEY, L. L., J. M. GOODRICH, D. MIQUELLE, E. N. SMIRNOV, H.
QUIGLEY, AND M. G. HORNOCKER. 2002. Effects of roads and human
disturbance on Amur tigers. Conservation Biology 16:97–108.
KERLEY, L. L., J. M. GOODRICH, D. MIQUELLE, E. N. SMIRNOV, H.
QUIGLEY, AND M. G. HORNOCKER. 2003. Reproductive parameters of
wild female Amur (Siberian) tigers (Panthera tigris altaica).
Journal of Mammalogy 84:288–298.
Vol. 94, No. 4
KNOPFF, K. H., A. A. KNOPFF, A. KORTELLO, AND M. S. BOYCE. 2010.
Cougar kill rate and prey composition in a multiprey system.
Journal of Wildlife Management 74:1435–1447.
KNOPFF, K. H., A. A. KNOPFF, M. B. WARREN, AND M. S. BOYCE. 2009.
Evaluating global positioning system telemetry techniques for
estimating cougar predation parameters. Journal of Wildlife
Management 73:586–597.
KOVALCHUK, N. I. 1988. The tiger diary. Hunting and Game
Management 11:33–35.
KUCHERENKO, S. P. 1977. The impact of the Amur tiger on the
population on its prey. Pp. 133–134 in Rare species of mammals
and their conservation (V. Y. Sokolov, ed.). Nauka Publishers,
Moscow, Russia.
KUCHERENKO, S. P. 1993. The price of tiger conservation. Hunting and
Game Management 2:16–19.
LEWIS, J. C. M., AND J. M. GOODRICH. 2009. Principles and practice of
field anaesthesia. Wildlife Conservation Society, New York, New
York.
LIU, C., P. M. BERRY, T. P. DAWSON, AND R. G. PEARSON. 2005.
Selecting thresholds of occurrence in the prediction of species
distributions. Ecography 28:385–393.
MAUTZ, W. W., AND P. J. PEKINS. 1989. Metabolic rate of bobcats as
influenced by seasonal temperatures. Journal of Wildlife Management 53:202–205.
MERRILL, E., ET AL. 2010. Building a mechanistic understanding of
predation with GPS-based movement data. Philosophical Transactions of the Royal Society of London, B. Biological Sciences
365:2279–2288.
METZ, M. C., D. W. SMITH, J. A. VUCETICH, D. R. STAHLER, AND R. O.
PETERSON. 2012. Seasonal patterns of predation for gray wolves in
the multi-prey system of Yellowstone National Park. Journal of
Animal Ecology 81:553–563.
METZ, M. C., J. A. VUCETICH, D. W. SMITH, D. R. STAHLER, AND R. O.
PETERSON. 2011. Effect of sociality and season on gray wolf (Canis
lupus) foraging behavior: implications for estimating summer kill
rate. PLoS ONE 6:e0017332.
MILLER, C. S., M. HEBBLEWHITE, J. M. GOODRICH, AND D. G. MIQUELLE.
2010. Review of research methodologies for tigers: telemetry.
Integrative Zoology 5:378–389.
MILLER, C. S., Y. K. PETRUNENKO, J. M. GOODRICH, M. HEBBLEWHITE, I.
V. SERYODKIN, AND D. G. MIQUELLE. 2011. Translocation a success,
but poaching remains a problem for Amur tigers. Cat News 55:22–
25.
MIQUELLE, D., I. G. NIKOLAEV, J. M. GOODRICH, M. N. LITVINOV, E. N.
SMIRNOV, AND E. SUVOROV. 2005a. Searching for the coexistence
recipe: a case of conflicts between people and tigers in the Russian
Far East. Pp. 305–322 in People and wildlife: conflict or
coexistence? (R. Woodroffe, S. Thirgood, and A. Rabinowitz,
eds.). Cambridge University Press, London, United Kingdom.
MIQUELLE, D., E. N. SMIRNOV, H. QUIGLEY, M. G. HORNOCKER, I. G.
NIKOLAEV, AND E. H. MATYUSHKIN. 1996. Food habits of Amur tigers
in Sikhote-Alin Zapovednik and the Russian Far East, and
implications for conservation. Journal of Wildlife Research
1:138–147.
MIQUELLE, D., ET AL. 2007. 2005 Amur tiger census. Cat News 46:14–
17.
MIQUELLE, D. G., E. N. SMIRNOV, G. A. SALKINA, AND V. K. ABRAMOV.
2005b. The importance of protected areas in Amur tiger
conservation: a comparison of tiger and prey abundance in
protected areas versus unprotected areas. Pp. 70–74 in Results of
protection and research of the Sikhote-Alin natural landscape.
August 2013
MILLER ET AL.—AMUR TIGER KILL AND CONSUMPTION RATES
Papers presented at the International Science and Management
Conference devoted to the 70th anniversary of the Sikhote-Alin
State Reserve, Terney, Russia, Primorksi Region, September 20–
23, 2005 (I. V. Potikha, et al., eds.). (in Russian with English
abstract).
MIQUELLE, D. G., ET AL. 1999a. A habitat protection plan for the Amur
tiger: developing political and ecological criteria for a viable landuse plan. Pp. 273–295 in Riding the tiger: meeting the needs of
people and wildlife in Asia (J. Seidensticker, S. Christie, and P.
Jackson, eds.). Cambridge University Press, Cambridge, United
Kingdom.
MIQUELLE, D. G., ET AL. 1999b. Hierarchical spatial analysis of Amur
tiger relationships to habitat and prey. Pp. 71–99 in Riding the tiger:
meeting the needs of people and wildlife in Asia (J. Seidensticker,
S. Christie, and P. Jackson, eds.). Cambridge University Press,
Cambridge, United Kingdom.
MIQUELLE, D. G., ET AL. 2010a. A monitoring program for the Amur
tiger thirteenth-year report: 1998–2010. Wildlife Conservation
Society 1–25, Vladivostok, Russia.
MIQUELLE, D. G., ET AL. 2010b. The Amur tiger: a case study of living
on the edge. Pp. 325–339 in Biology and conservation of wild felids
(D. W. Macdonald and A. J. Loveridge, eds.). Oxford University
Press, Oxford, United Kingdom.
NOWELL, K. 2000. Far from a cure: the tiger trade revisited. Traffic
International, Cambridge, United Kingdom.
PIKUNOV, D. G. 1983. The Amur tiger and its impact on the wild
ungulates of Primorie. Pp. 128–129 in Proceedings of the 3rd AllUnion Symposium, Rare Mammal Species of USSR and their
Conservation. Moscow, Russia.
PIKUNOV, D. G. 1988. Eating habits of the Amur tiger (Panthera tigris
altaica) in the wild. Pp. 185–190 in Proceedings of the 4th World
Conference on Breeding Endangered Species in Captivity (B. L.
Dresser, ed.). Cincinnati Zoo and Botanical Garden Center,
Cincinnati, Ohio.
ROZHNOV, V. V., ET AL. 2011. Application of satellite collars to the
study of home range and activity of the Amur tiger (Panthera tigris
altaica). Biology Bulletin 38:834–847.
SAND, H., P. WABAKKEN, B. ZIMMERMANN, Ö. JOHANSSON, H. C.
PEDERSEN, AND O. LIBERG. 2008. Summer kill rates and predation
pattern in a wolf–moose system: can we rely on winter estimates?
Oecologia 156:53–64.
SCHALLER, G. B. 1967. The deer and the tiger. University of Chicago
Press, Chicago, Illinois.
SEIDENSTICKER, J. 1976. Ecological separation between tigers and
leopards. Biotropica 8:225–234.
SIKES, R. S., W. L. GANNON, AND THE ANIMAL CARE AND USE COMMITTEE
OF THE AMERICAN SOCIETY OF MAMMALOGISTS. 2011. Guidelines of
the American Society of Mammalogists for the use of wild
mammals in research. Journal of Mammalogy 92:235–253.
0
SUNQUIST, M. E. 1981. The social organization of tigers (Panthera
tigris) in Royal Chitawan National Park, Nepal. Smithsonian
Contributions to Zoology 336:1–98.
SUNQUIST, M. E., K. U. KARANTH, AND F. SUNQUIST. 1999. Ecology,
behavior and resilience of the tiger and its conservation needs. Pp.
5–18 in Riding the tiger: tiger conservation in human-dominated
landscapes (J. Seidensticker, S. Christie, and P. Jackson, eds.).
Cambridge University Press, Cambridge, United Kingdom.
TAMBLING, C. J., S. D. LAURENCE, S. E. BELLAN, E. Z. CAMERON, J. T.
DU TOIT, AND W. M. GETZ. 2012. Estimating carnivoran diets using a
combination of carcass observations and scats from GPS clusters.
Journal of Zoology (London) 286:102–109.
THOMPSON, S. K. 2002. Sampling. Wiley-Interscience, Hoboken, New
Jersey.
VASILIEV, N. G., AND I. A. FLIAGINA. 2006. Vegetation cover. Pp. 122–
219 in Plants and animals of the Sikhote-Alin Reserve (A. A.
Astafiev, ed.). Vladivostok, Russia.
WALSTON, J., ET AL. 2010. Bringing the tiger back from the brink—the
six percent solution. PLoS Biology 8:e1000485.
WEBB, N. F., M. HEBBLEWHITE, AND E. H. MERRILL. 2008. Statistical
methods for identifying wolf kill sites using global positioning
system locations. Journal of Wildlife Management 72:798–807.
WIKRAMANAYAKE, E. D., ET AL. 1998. An ecology-based method for
defining priorities for large mammal conservation: the tiger as case
study. Conservation Biology 12:865–878.
WILMERS, C. C., R. L. CRABTREE, D. W. SMITH, K. M. MURPHY, AND W.
M. GETZ. 2003. Trophic facilitation by introduced top predators:
grey wolf subsidies to scavengers in Yellowstone National Park.
Journal of Animal Ecology 72:909–916.
WORLD WILDLIFE FUND. 2002. An analysis of the effectiveness of the
Amur tiger anti-poaching brigades in the Russian Far East. World
Wildlife Fund, Vladivostok, Russia.
YUDAKOV, A. G. 1973. The tigers’ impact on the numbers of ungulates.
Pp. 93–94 in Rare mammal species of the fauna of the USSR and
their conservation. Nauka Publishers, Moscow, Russia.
YUDAKOV, A. G., AND I. G. NIKOLAEV. 1987. The ecology of the Amur
tiger: based upon winter obersvations at a field station in the west
central Sikhote-Alin between 1970–1973. Nauka, Moscow, Russia.
YUDIN, V. G. 1990. Foraging ecology of the tigers. Hunting and Game
Management 11:10–13.
ZHIVOTCHENKO, V. I. 1979. The number of ungulates harvested
annually by a family of tigers, The ecological basis for protection
and rational use of carnivorous mammals. Materials from an AllSoviet Conference. Nauka, Moscow, Russia. Pp. 246–247.
ZIMMERMANN, B., P. WABAKKEN, H. SAND, H. C. PEDERSEN, AND O.
LIBERG. 2007. Wolf movement patterns: a key to estimation of kill
rate? Journal of Wildlife Management 71:1177–1182.
Submitted 20 August 2012. Accepted 21 January 2013.
Associate Editor was Roger A. Powell.